Foreign Direct Investment in India:

Assessing Inter-State Variations Introduction

Sources: Tufts Geodata Database, India Stat

Class: Intro to GIS 101, Fall 2015

Project Coordination: WGS _1984 UTM Zone 44

Cartography: Adrienne Larson

I created individual layers based on my nine independent variables of state level data: GDP, minimum

wage (both in Indian Rupees) GDP growth per capita, education quality, urban share of population, power

availability per capita, average road density, and average national highway density. I also created a dum-

my variable- “four state”- to represent the states in which there was one of the four most populous cities in

India: Mumbai, New Delhi, Chennai, and Bangalore. These cities are present in the states of Maharashta,

Delhi, Karnataka, and Tamil Nadu. I then ran a least squares regression to determine the effect of each ex-

planatory variable on FDI. I found that two of my variables– GDP and presence in a state with one of the

four most populous cities– were statistically significant. Power availability was also highly correlated with

FDI inflow, while minimum wage and education quality showed no significant correlation. Based on these

results, I also created a map showing the largest cities in India (over 2 million people) by population size

and FDI inflow between 1991 and 2001.

Foreign direct investment (FDI) is as a significant driver of development for low and middle-income coun-

tries. Inflows of FDI bridge the gap between the desired and the actual level of capital stock, especially

when domestic investment is not sufficient to push the actual capital stock up to the desired level. The lit-

erature shows certain factors are significant determinants of FDI inflows to a region, including infrastruc-

ture level, average education level of the population, labor cost, GDP, GDP growth rate, and the level of

political stability. In India, there is great amount of variation in the value of FDI inflow by state, with 90%

of FDI going to only five states. I sought to investigate why certain states received more investment than

others, and whether these patterns were spatial or not. A better understanding of these differences is im-

portant for promoting balanced regional development in the country, especially given the observed rela-

tionship between FDI and increased economic development. Moreover, I hope that this research could be

used in understanding the spatial determinants and regional variation of foreign investment in other devel-

oping countries similar to India.

GDP (Indian Rs) GDP Growth Rate (%) Power Availability Per Capita (Kwh) Minimum Wage (Indian Rs)

Because I only had FDI values on the state level for 28 states, I was not able to analyze the data for the re-

maining Indian states. Such a small sample size made it difficult to produce significant results. My study

was also restricted by the fact that the data for the independent variables came from different years. Alt-

hough my FDI data was an aggregate sum of FDI inflow over the decade 1991-2001, none of my inde-

pendent variables (with the exception of power availability per capita) spanned this exact time frame. In-

stead, datasets were from different years in the 1990’s or early 2000’s, and spanned different time intervals.

Given that this decade was a period of high economic growth and significant change in India, it is likely

that some of the data I did not include was significant, as some of my variables could have been changing

on an annual basis. Therefore, it is difficult to draw conclusions from data representing different time

frames. Another main area that restricted the potential usefulness of my results was the fact that my data

was not annual. FDI often changes year to year as a result of changes in government policy or economic

growth level, and therefore it would have been better to analyze it annually. Finally, my FDI data only

showed FDI inflow overall, and not investment by sector. Therefore, my analysis could not capture differ-

ences in FDI inflow to the technology, manufacturing, or other industries, which may have shown reveal-

ing trends or changed the significance of my earlier results.

My data confirmed conclusions from the literature that GDP and proximity to major cities are two major

determinants of FDI. This is likely due to the benefits associated with populous cities and large capital

stock, such as economies of agglomeration and positive network effects. Both of these factors would make

a location attractive to foreign companies looking to invest in a particular state in India. Moreover, power

availability showed a high correlation with FDI, likely due to the necessity of power in running businesses.

These conclusions are relevant for policy in that they allow relevant stakeholders to better understand the

principle draws for foreign investors. Further research should be done with annual data to better analyze

the relationship between other spatial factors of interest, such as railroad density or elevation. Another area

for further investigation would be the impact of human capital, measured in tertiary education level. It may

be more revealing to only look at quality of tertiary education rather than education level overall, since this

may be a better indicator of how well a labor force is trained. These results may also be useful in under-

standing the regional disparities in FDI in countries similar to India. Given that FDI inflow significantly

impacts a country’s development level, it will no doubt continue to be an important area of study for policy

makers in the coming decades as low and middle-income countries continue to develop.

Methods & Results

Limitations

Conclusion

References

Education Quality Index (0-1) Highway Average Density (Km/ Sq Km)